Technology is helping hospitals there to manage huge patient loads while still achieving more efficient, preemptive and personalized medical care.
Among the many fields of medicine in India, endocrinology is benefiting most from the benefits of AI’s latest breakthroughs.
By harnessing the power of machine learning algorithms and data analytics, AI can be used by doctors to process huge amounts of patient information, ranging from medical records to lifestyle data, to facilitate early identification of endocrine problems, personalized treatment plans and optimized patient outcomes.
Shedding light on the pivotal role that AI is playing in the diagnosis and management of diabetes, Dr Saroj Gupta, CEO and founder, MyDigiRecords, said: “AI is seamlessly ushering in a new era of personalized and data-driven healthcare. It is revolutionizing the landscape of diabetes care, offering life-altering solutions in both diagnosis and management.”
Dr Dheeraj Kapoor, Chief of Endocrinology, Artemis Hospital Gurugram, added: “The automation with AI helps doctors to quickly sift through patient reports, checking lifestyle factors and genetic material in detail for early detection of the issue and immediate commencement of therapy. AI tools accelerate precise diagnosis of patients, constant surveillance, personalized cure and better delivery of patient care.”
Another doctor in Artemis Hospital, Dr Shivanshu Raj Goyal, Consultant (Respiratory/Pulmonology & Sleep Medicine), noted: “It has also streamlined administrative tasks, reining in operational costs. However, despite these advancements, challenges related to data privacy and ethical concerns must be dealt with to maximize the effectiveness of AI in healthcare.”
Tackling diabetes with AI
Medical practitioners can use various AI models and algorithms to distinguish between Type 1 and Type 2 diabetes and devise treatment plans accordingly, taking genetic profiles and individualized responses to medications into account.
Predictive modeling can also be used to identify potential challenges before and during treatment, reducing medical and patient-care variables for faster and more effective outcomes.
According to Dr Kapoor, in this context, classical ML models such as the Decision Tree; Random Forest; Support Vector Machines, K-Nearest Neighbor and ensemble-learning models are widely used in India. Also, “to monitor glucose levels and predict the perils of diabetes, ‘recurrent neural networks’ and ‘long short-term memory’ networks are most frequently used. Furthermore, decision trees and support vector machines aid in diabetes diagnosis. Also, closed-loop systems that incorporate control algorithms like ‘proportional-integral-derivative’ controllers assist in regulating insulin delivery based on real-time data, in turn resulting in effective management,” he said.
Other conditions such as diabetic retinopathy are also being addressed by doctors with AI to improve management and medical outcomes. In case of retinopathy, AI algorithms process retinal scans, inspecting subtle changes indicative of diabetic damage to the eyes.
“Deep-learning algorithms, especially ‘convolutional neural networks’, are commonly used to spot the issue efficiently as they aid in the analysis of retinal images and identifying abnormalities. This eventually enables doctors to prevent the potential loss of vision,” said Dr Kapoor.
Furthermore, AI-based diagnostic tools can assist doctors in assessing diabetic neuropathy by gauging nerve function. According to Dr Gupta, “early detection is crucial in preventing vision loss and underscores the potential of AI in backing healthcare professionals in specialized domains such as ophthalmology.”
When diabetes affects other organs
Other than the kidneys and the eyes, diabetes that has not been addressed well over a longer term can affect the large blood vessels of the heart, brain and legs, and also cause damage to the small blood vessels in the feet and the nerves.
At this advanced stage of the disease, AI can also be used to manage the diabetes-related complications. Advanced imaging techniques coupled with AI analysis enable the identification of early signs of diabetes-linked vascular and neurological complications.
AI can speed up doctors’ analysis of magnetic resonance imaging and/or computed tomography images to identify signs of diabetes-linked atherosclerosis or vascular abnormalities. “AI has immense possibilities, but coordination with healthcare professionals is crucial for accurate interpretation and better management of diabetes-related problems in the various organs,” Dr Kapoor noted.
Addressing patient compliance and monitoring
Dr Kapoor also noted how smart technology is being used in the country to keep a watch on patient compliance and progress. Smart watches endowed with AI capabilities can probe and update physiological data for automatic monitoring and alerts by doctors.
“Companies like Google and Apple are actively incorporating hi-tech AI features into their smartwatches to enhance health monitoring. Many specialized smartwatches have a feature for continuous glucose monitoring that support diabetes management by providing valuable information for timely medical attention,” Dr Kapoor said, noting that, while such automation empowers people to collect personal health data, the privacy of the data must also be protected from unauthorized usage.
